Co-constructing knowledge with youth: what high-school aged mentors say and do to support their mentees’ autonomy, belonging, and competence
Bibliographic record
Abstract
Self-Determination Theory’s (SDT) most recent ‘mini-theory,’ Relationships Motivation Theory (RMT) focuses on the essential ingredients of high-quality relationships (i.e. feelings of autonomy, belonging, and competence). This study explores the applicability of RMT to cross-age peer mentoring. Of particular interest was whether the RMT framework could help high-school mentors develop positive relationships with their elementary-aged mentees. The specific language and strategies mentors used to support feelings of autonomy, belonging, and competence was also of interest, as this level of detail has not been captured in previous research. High-school mentors were invited to learn about RMT during skill-building sessions. They were then asked to apply the language and skills they co-developed during mentoring sessions. Data included audio recordings of dyadic interactions, weekly mentoring logs, and interviews. Descriptive, Provisional, and In-Vivo coding were used to analyze data. Qualitative coding indicated high-school mentors were capable of co-constructing language and practices to support mentees’ feelings of autonomy, belonging, and competence. Findings also indicated that mentors successfully applied this knowledge to mentoring sessions. Weekly mentoring logs indicated skill-building sessions helped mentors develop positive relationships with their mentees. The results of this study begin to suggest that RMT can help inform the cross-age peer mentoring process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".